Central limit theorem for supercritical binary homogeneous Crump-Mode-Jagers processes

Benoît Henry (1, 2)

1 TOSCA - TO Simulate and CAlibrate stochastic models
2 IECL - Institut Élie Cartan de Lorraine

We consider a supercritical general branching population where the lifetimes of individuals are i.i.d. with arbitrary distribution and each individual gives birth to new individuals at Poisson times independently from each others. The population counting process of such population is a known as binary homogeneous Crump-Jargers-Mode process. It is known that such processes converges almost surely when correctly renormalized. In this paper, we study the error of this convergence. To this end, we use classical renewal theory and recent works [17, 6, 5] on this model to obtain the moments of the error. Then, we can precisely study the asymptotic behaviour of these moments thanks to Lévy processes theory. These results in conjunction with a new decomposition of the splitting trees allow us to obtain a central limit theorem. MSC 2000 subject classifications: Primary 60J80; secondary 92D10, 60J85, 60G51, 60K15, 60F05.

Mots clés: central limit theorem; CMJ processes; branching processes; splitting tree

Domaines: Probabilités [math.PR]

Benoît Henry. Central limit theorem for supercritical binary homogeneous Crump-Mode-Jagers processes. 2016. ⟨hal-01202095v2⟩